code: def testDropoutFCLayers(self, training):
      batch_size = 3
      num_obs_dims = 5
      obs_spec = tensor_spec.TensorSpec([num_obs_dims], tf.float32)
      network = encoding_network.EncodingNetwork(
          obs_spec, fc_layer_params=[20], dropout_layer_params=[0.5]
      )
      obs = tf.random.uniform([batch_size, num_obs_dims])
      output1, _ = network(obs, training=training)
      output2, _ = network(obs, training=training)
      self.evaluate(tf.compat.v1.global_variables_initializer())
      output1, output2 = self.evaluate([output1, output2])
      if training:
          self.assertGreater(np.linalg.norm(output1 - output2), 0)
      else:
          self.assertAllEqual(output1, output2)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.networks import encoding_network
  from tf_agents.specs import tensor_spec
  from tf_agents.utils import test_utils
label_desc: Use image node_id 16 for calling the EncodingNetworkTest obj's underlying member method code with example usage: obj.testDropoutFCLayers(training) without return types